
What happened
Nvidia invested $2 billion(約3200億円) in Marvell Technology in March 2026 and announced a strategic partnership covering custom AI chips, NVLink Fusion-compatible networking, optical interconnects, and silicon photonics. Marvell develops technology enabling customers to build custom accelerators and networks—including around UALink, an open interconnect standard designed as an alternative to Nvidia's proprietary NVLink fabric.
Why it matters
Hyperscalers (large cloud providers like Amazon, Alphabet, Meta, and Microsoft) are developing custom chips to reduce dependence on Nvidia GPUs. Rather than treat this as a threat, Nvidia is using NVLink Fusion to keep its networking at the center of hybrid systems where custom processors coexist with Nvidia's infrastructure. Marvell's value lies in its ability to help customers design processors and the connectivity required to make them work—meaning it can serve customers choosing either UALink or Nvidia's ecosystem, or both for different workloads.
What to watch
The investment signals that as AI systems scale from individual servers to racks with dozens of accelerators and clusters with hundreds of thousands of chips, moving data between processors is becoming as important as processing power itself. Marvell is positioned to solve this bottleneck across copper, optical, and signal-processing layers. In April 2026, Marvell acquired Polariton Technologies to advance its optical roadmap toward 3.2T connections and beyond—but the company must still convert its engineering position into durable revenue and compete against Broadcom and others.
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The investment appears paradoxical on the surface—why would Nvidia fund a company helping build alternatives to its own interconnect fabric? The answer lies in how the AI infrastructure market is evolving. Hyperscalers including Amazon, Alphabet, Meta, and Microsoft are developing custom silicon tailored to specific workloads, reducing their dependence on Nvidia GPUs as general-purpose accelerators. Nvidia could treat this customization as a competitive threat. Instead, NVLink Fusion allows Nvidia to participate in hybrid systems where custom processors coexist with its networking infrastructure, even if Nvidia does not manufacture every chip in the stack.
Marvell's strategic position is unusual because it operates across both paths. It helps customers design custom silicon and develop the connectivity technologies—electrical interfaces, switches, optical signal processors, and silicon photonics—needed to make those systems work. This means a customer can choose a non-Nvidia processor without necessarily abandoning Nvidia's interconnect, or choose an open standard like UALink for greater supplier flexibility. Marvell's business succeeds as long as customers need custom processors and the connectivity required to connect them, regardless of which fabric they select.
The technical driver underpinning this shift is the changing bottleneck in AI data centers. The first stage of the AI boom centered on computing power—adding more accelerators to train larger models. But as systems scale from individual servers to racks containing dozens of accelerators and clusters containing hundreds of thousands of chips, the problem shifts. Moving data between processors becomes almost as important as processing it. Nvidia's Rubin generation will deliver more computing power, which means more traffic moving among processors, memory, switches, and storage. Each increase in computing density puts greater pressure on the surrounding network. Marvell's portfolio of SerDes technology, switches, optical DSPs, and custom-silicon services is positioned to solve different parts of this interconnection problem.
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